Spatially Invariant Vector Quantization: A pattern matching algorithm for multiple classes of image subject matter including pathology.
Spatially Invariant Vector Quantization: A pattern matching algorithm for multiple classes of image subject matter including pathology.
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DOI：
10.4103/2153-3539.77175
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发表时间：
2011-02-26
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通讯作者：
Balis, Ulysses J
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作者：
Hipp, Jason D;Cheng, Jerome Y;Balis, Ulysses J
前言：历史上，图像分析和模式识别算法在病理学中的有效临床应用一直受到两个关键限制的阻碍：1)整个数字切片图像数据集的可用性；2)执业病理学家在应用此类算法方面的相对领域知识不足。随着最近快速采用整体幻灯片成像解决方案的到来，以前的限制在很大程度上得到了解决。然而，预计当代病理学家的一般队列不太可能在短期内获得先进的图像分析技能，后一个问题仍然存在，因此需要一种同时具有图像领域(或器官系统)独立性和极易使用的属性的算法，而不需要专门的培训或专业知识。结果：在本报告中，我们提出了一种新颖的、通用的病例模式识别算法，空间不变矢量量化(SIVQ)，它克服了上述知识不足。从根本上讲，基于传统的矢量量化(VQ)模式识别方法，SIVQ通过使用表现出连续对称性的环形向量而不是方形或矩形向量来获得其优越的性能和本质上的零训练工作流模型。通过使用连续对称性固有的随机匹配特性，与传统的VQ矢量相比，单环矢量在匹配可能性方面可以表现出多达一百万倍的改进。SIVQ被用于在广泛的粗略和微观用例设置中展示快速和高精度的模式识别能力。结论：根据到目前为止观察到的SIVQ的性能，我们发现确实存在适合于部署在这样的环境中的图像分析/模式识别算法类别，其中只有病理学家可以有效地将其用于临床工作流程，作为交钥匙解决方案。我们预计，SIVQ和其他相关的独立于类的模式识别算法将成为整个数字图像分析方法的一部分，执业病理学家可以立即获得这些方法，而不需要立即获得图像分析专家。
INTRODUCTION: HISTORICALLY, EFFECTIVE CLINICAL UTILIZATION OF IMAGE ANALYSIS AND PATTERN RECOGNITION ALGORITHMS IN PATHOLOGY HAS BEEN HAMPERED BY TWO CRITICAL LIMITATIONS: 1) the availability of digital whole slide imagery data sets and 2) a relative domain knowledge deficit in terms of application of such algorithms, on the part of practicing pathologists. With the advent of the recent and rapid adoption of whole slide imaging solutions, the former limitation has been largely resolved. However, with the expectation that it is unlikely for the general cohort of contemporary pathologists to gain advanced image analysis skills in the short term, the latter problem remains, thus underscoring the need for a class of algorithm that has the concurrent properties of image domain (or organ system) independence and extreme ease of use, without the need for specialized training or expertise.RESULTS: In this report, we present a novel, general case pattern recognition algorithm, Spatially Invariant Vector Quantization (SIVQ), that overcomes the aforementioned knowledge deficit. Fundamentally based on conventional Vector Quantization (VQ) pattern recognition approaches, SIVQ gains its superior performance and essentially zero-training workflow model from its use of ring vectors, which exhibit continuous symmetry, as opposed to square or rectangular vectors, which do not. By use of the stochastic matching properties inherent in continuous symmetry, a single ring vector can exhibit as much as a millionfold improvement in matching possibilities, as opposed to conventional VQ vectors. SIVQ was utilized to demonstrate rapid and highly precise pattern recognition capability in a broad range of gross and microscopic use-case settings.CONCLUSION: With the performance of SIVQ observed thus far, we find evidence that indeed there exist classes of image analysis/pattern recognition algorithms suitable for deployment in settings where pathologists alone can effectively incorporate their use into clinical workflow, as a turnkey solution. We anticipate that SIVQ, and other related class-independent pattern recognition algorithms, will become part of the overall armamentarium of digital image analysis approaches that are immediately available to practicing pathologists, without the need for the immediate availability of an image analysis expert.